Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs.

Journal: Computers in biology and medicine
PMID: 40056835

Abstract

BACKGROUND: Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the detection of potential diseases. Despite their usefulness, CAD systems cannot yet fully replace doctors in diagnosing many conditions due to limitations in current algorithms. Cerebral microbleeds (CMBs) are a critical area of concern for neurological health, and accurate detection of CMBs is essential for understanding their impact on brain function. This study aims to improve CMB detection by enhancing existing machine learning algorithms.

Authors

  • Behrang Khaffafi
    Department of Medicine, Urmia University of Medical Sciences, Urmia, Iran. Electronic address: Khaffafi.b@umsu.ac.ir.
  • Hadi Khoshakhalgh
    Department of Medicine, Urmia University of Medical Sciences, Urmia, Iran. Electronic address: khoshakhlagh.h@umsu.ac.ir.
  • Mohammad Keyhanazar
    Department of Electrical and Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. Electronic address: m.keyhanazar@gmail.com.
  • Ehsan Mostafapour
    Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Electronic address: e.mostafapour@urmia.ac.ir.

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